Traditional route planning schemes that work well for guiding an autonomous vehicle through congested areas with other moving vehicles, stationary obstacles, etc. can be expected to perform well over only relatively short distances, since they typically must concern themselves with maintaining an internal representation of the area being traversed at a fine level of detail, and must update that representation at a high rate in order to accurately model and respond to the environment. One such planner is described in the '321 reference. Traditional route planners that work well for planning routes over any distance are typically ill-equipped to handle vehicle guidance tasks in a dynamically changing, congested environment. It is desired to have a system that can perform at both fine and coarse levels of detail, and account for the dynamic changes to the environment that may affect the route that the vehicle may take. It is further desired to have a system that retains information obtained about the actual traversal of a path, so that improved estimates can be used to plan future routes that may coincide with all or a portion of any path already traversed. Moreover, it is desired to be able to perform analysis of coarse-level route connections to make automated inferences about potential paths.